PD5163 Sample Summary

## `summarise()` has grouped output by 'patient', 'age_at_sample_exact', 'age_at_sample', 'DOB', 'DATE_OF_DIAGNOSIS'. You can override using the `.groups` argument.
## Joining, by = "PDID"
patient ID age_at_sample_exact cell_type phase BaitLabel
2 PD5163 PD5163e 31.87406 PB Gran Recapture PD5163e
3 PD5163 PD5163f 35.59206 PB Gran Recapture PD5163f
1 PD5163 COLONY37 37.14716 BFU-E-Colony Colony NA
4 PD5163 PD5163g 37.87269 PB Gran Recapture PD5163g
7 PD5163 PD5163b4 37.87269 T cell Recapture b4
5 PD5163 PD5163h 40.40246 PB Gran Recapture PD5163h
6 PD5163 PD5163b3 NA Buccal Recapture b3

Tree

tree=plot_basic_tree(PD$pdx,label = PD$patient,style="classic")

Expanded Tree with Node Labels

The nodes in this plot can be cross-referenced with nodes specified in subsequent results. The plot also serves to give an idea of what the topology at the top of the tree looks like.

tree=plot_basic_tree(expand_short_branches(PD$pdx,prop = 0.1),label = PD$patient,style="classic")
node_labels(tree)

Timing of driver mutations (using Model = poisson_tree )

Note that the different colours on the tree indicate the separately fitted mutation rate clades.

Driver Specific Mutation Rates & Telomere Lengths by Colony & Timepoint

## 
## Random-Effects Model (k = 1; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  -0.0000    0.0000    4.0000      -Inf   16.0000   
## 
## tau^2 (estimated amount of total heterogeneity): 0
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  18.1834  0.3697  49.1795  <.0001  17.4588  18.9081  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
node driver status child_count type colony_count mean_lambda_rescaled correction sd_rescaled lb_rescaled ub_rescaled median_rescaled p_lt_wt
-1 WT 1 -1 local 55 18.18345 1 0.1367051 17.91952 18.45608 18.18253 NA
103 JAK2 1 6 local 6 19.33665 1 0.6150426 18.19465 20.61581 19.31579 0.028325
110 DNMT3A 1 8 local 8 21.52251 1 0.6723947 20.30200 22.96595 21.48660 0.000025
13 chrX_DEL 0 1 local 1 18.98204 1 3.2404927 12.50588 26.28242 18.75261 0.400600

Driver Acquisition Timeline

All ages are in terms of post conception years. The vertical red lines denote when colonies were sampled and blue lines when targeted follow up samples were taken.

patient node driver child_count lower_median upper_median lower_lb95 lower_ub95 upper_lb95 upper_ub95 N group age_at_diagnosis_pcy max_age_at_sample min_age_at_sample
PD5163 110 DNMT3A 8 0.0600579 8.307589 0.0320775 0.1034081 6.988003 9.928769 6 DNMT3A 32.00274 41.13073 32.60233
PD5163 103 JAK2 6 0.0598308 9.276072 0.0346673 0.1027530 7.947406 10.747222 6 JAK2 32.00274 41.13073 32.60233

Copy Number Variation and Timing

Summary of LOH timing inference

Duplications?

VAF Distribution of Targeted Follow Up Samples

Here we exclude all local CNAs and depict as color VAF plots